The Center for Genomic Sciences of the National Autonomous University of Mexico (UNAM) in Cuernavaca and a group of collaborators propose to further develop and enrich the RegulonDB database. RegulonDB contains the largest electronically-available regulatory network of any living organism, that of Escherichia coli K-12. In the project we will expand the design, implement computational and conceptual models, and generate annotations derived from original literature of the different components of the regulatory network. We will experimentally map promoters and transcription units under different conditions. This project expands a previous grant supporting RegulonDB. High quality manual curation is to be enriched beyond transcription initiation to include signal-transduction connecting sensing with regulation, as well as with natural language text processing tools. We propose to continue an exhaustive experimental mapping of all possible transcription initiation sites in the genome and to perform an exhaustive experimental identification of transcription units in different conditions. The core of RegulonDB (literature-based network and the graphic and browsing tools) will be linked and made accessible, as optional user selections, with high-throughput available experiments (microarrays and ChIP- chip) both as raw and pre-analyzed data, as well as with collections of computational predictions of all network elements (transcription factors, promoters, binding sites, operons, regulatory interactions). This will be based on track representations and compliant with international consortia normalized pathway exchange formats. The openness of RegulonDB data and internal structure will expand considerably, enhancing as well its availability to tools for analysis and representation. PUBLIC HEALTH RELEVANCE: RegulonDB is a gold standard both in its conceptual and computational design, as well as in the amount and quality of data gathered on the largest and best characterized regulatory network of a single organism. The improvements and expansion of RegulonDB may accelerate research in other bacterial models, including pathogens. RegulonDB has served as a benchmark for a variety of computational predictive tools for components of the network. Furthermore, E.coli is at the forefront in systems biology as a proof-of-concept biological model to develop new methodologies in bioinformatics, representation and simulation, to overcome challenges in order to model and understand the functioning of a whole cell.